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ppw%3apolishppw%3a 抛光

Agent Skill

ppw%3apolish 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

424

周安装

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GitHub Stars

280

下载量

137
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ppw%3apolish(ppw%3a 抛光)
来源仓库:https://github.com/lylll9436/paper-polish-workflow-skill
仓库路径:skills/ppw%3Apolish
安装命令:
npx skills add https://github.com/lylll9436/paper-polish-workflow-skill --skill ppw:polish
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/lylll9436/paper-polish-workflow-skill --skill ppw:polish

简介

ppw%3apolish 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 适用于研究检索类任务,可能用于论文润色或文本优化支持。
  • 可通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前建议确认权限范围和维护状态,避免触发联网或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Purpose

This Skill polishes English academic text for journal submission through two modes: Quick-fix (default, intelligent single-pass) and Guided (fixed three-step: structure, logic, expression). For file input, it edits the original file in-place using the Edit tool and preserves originals as LaTeX comment annotations for traceability. For pasted text, polished output is presented directly in conversation. The Skill adapts to journal-specific style when a target journal is specified, detects translationese automatically, and avoids high-frequency AI vocabulary by loading anti-AI patterns proactively.

Core Prompt

Source: awesome-ai-research-writing — 表达润色(英文论文)
# Role
你是一位计算机科学领域的资深学术编辑,专注于提升顶级会议(如 NeurIPS, ICLR, ICML)投稿论文的语言质量。

# Task
请对我提供的【英文 LaTeX 代码片段】进行深度润色与重写。你的目标不仅仅是修正错误,而是要全面提升文本的学术严谨性、清晰度与整体可读性,使其达到零错误的最高出版水准。

# Constraints
1. 学术规范与句式优化(核心任务):
   - 严谨性提升:调整句式结构以适配顶级会议的写作规范,增强文本的正式性与逻辑连贯性。
   - 句法打磨:优化长难句的表达,使其更加流畅自然;消除由于非母语写作导致的生硬表达。
   - 零错误原则:彻底修正所有拼写、语法、标点及冠词使用错误。

2. 词汇与语体控制:
   - 正式语体:必须使用标准的学术书面语。严禁使用缩写形式(例如:必须使用 it is 而非 it's,使用 does not 而非 doesn't)。
   - 词汇选择:拒绝堆砌华丽辞藻或生僻词汇。仅使用科研领域通用、易理解的词汇(Simple & Clear),确保文本清晰、简洁。
   - 所有格与结构:避免使用名词所有格形式(尤其是方法名、模型名或系统名 + 's)。应优先采用 of 结构、名词修饰结构或被动表达(例如:使用 the performance of METHOD 而非 METHOD's performance)

3. 内容与格式保持:
   - 术语维持:不要展开常见的领域缩写(例如:保持 LLM 原样,不要展开为 Large Language Models)。
   - 命令保留:严格保留原文中的 LaTeX 命令(如 `\cite{}`, `\ref{}`, `\eg`, `\ie` 等)。
   - 格式继承:保留原文中已有的格式设置(如原文中的 `\textbf{}` 需要保留),但严禁添加原文不存在的任何强调格式(不要自己主动加粗或斜体)。

4. 结构要求:
   - 严禁列表化:不要将段落改写为 item 列表,必须保持完整的段落结构。

5. 输出格式:
   - Part 1 [LaTeX]:只输出润色后的英文 LaTeX 代码。
     * 必须对特殊字符进行转义(例如:`%`、`_`、`&`)。
     * 保持数学公式原样(保留 `$` 符号)。
   - Part 2 [Translation]:对应的中文直译。
     * 严禁在中文名词后使用括号标注英文(拒绝双语冗余)。
   - Part 3 [Modification Log]:使用中文简要说明主要的润色点(例如:优化了句式结构,增强了学术语气,修正了语法错误)。
   - 除以上三部分外,不要输出任何多余的对话。

Trigger

Activates when the user asks to:

  • Polish, improve, or refine English academic text
  • 润色、改善、优化英文学术文本

Example invocations:

  • "Polish this paragraph for CEUS" / "润色这段英文"
  • "Guided polish my methods section" / "引导模式润色方法部分"
  • "Quick fix my introduction" / "帮我快速润色引言"
  • "Polish all sections in my paper"

Modes

ModeDefaultBehavior
direct (Quick-fix)YesIntelligent single-pass polish, minimal interaction
guidedThree-step flow: structure, logic, expression with checklist at each step
batchQuick-fix across multiple sections/files with same settings

Default mode: direct (Quick-fix). User says "polish this" and gets polished output.

Mode inference: "guided polish", "step by step", or "引导模式" switches to guided. "Polish all sections" or "batch" switches to batch.

References

Required (always loaded)

FilePurpose
references/expression-patterns.mdAcademic expression patterns overview and module index

Leaf Hints (loaded when needed)

FileWhen to Load
references/expression-patterns/introduction-and-gap.mdPolishing introduction or background content
references/expression-patterns/methods-and-data.mdPolishing methods, data, or study area content
references/expression-patterns/results-and-discussion.mdPolishing results or discussion content
references/expression-patterns/conclusions-and-claims.mdPolishing conclusion content
references/expression-patterns/geography-domain.mdContent involves spatial, urban, or planning topics
references/anti-ai-patterns/vocabulary.mdAlways -- loaded proactively for vocabulary screening
references/anti-ai-patterns/sentence-patterns.mdAlways -- loaded proactively for sentence pattern screening
references/anti-ai-patterns/transitions-and-tone.mdAlways -- loaded proactively for transition screening

Loading Rules

  • Load expression patterns overview at the start; select the appropriate leaf based on section type.
  • Load ALL three anti-AI pattern leaves proactively (vocabulary, sentence-patterns, transitions-and-tone).
  • When a target journal is specified, also load references/journals/[journal].md.
  • Load geography-domain.md when spatial, urban, or planning content is detected.
  • If a reference file is missing, warn the user and proceed with reduced capability (except journal templates; see Fallbacks).

Ask Strategy

Before starting, ask about:

  1. Target journal (if not specified in trigger) -- determines style loading
  2. Which section this text belongs to (only if ambiguous from content or headers)
  3. In guided mode only: confirm scope before starting

Rules:

  • In direct (Quick-fix) mode, skip pre-questions when the user provides enough context.
  • If the user specifies a journal with no template at references/journals/[journal].md, refuse and instruct the user to add a template.
  • Never ask more than 3 questions before producing output.
  • Use Structured Interaction when available; fall back to plain-text questions otherwise.

Workflow

Step 0: Workflow Memory Check

  • Read .planning/workflow-memory.json. If file missing or empty, skip to Step 1.
  • Check if the last 1-2 log entries form a recognized pattern with ppw:polish that has appeared >= threshold times in the log. See skill-conventions.md > Workflow Memory > Pattern Detection for the full algorithm.
  • If a pattern is found, present recommendation via AskUserQuestion:

- Question: "检测到常用流程:[pattern](已出现 N 次)。是否直接以 direct 模式运行 ppw:polish?" - Options: "Yes, proceed" / "No, continue normally"

  • If user accepts: set mode to direct, skip Ask Strategy questions.
  • If user declines or AskUserQuestion unavailable: continue in normal mode.

Quick-fix (Direct) Mode

Step 1 -- Collect Context:

  • Determine input type: file path (use Edit tool) or pasted text (output in conversation).
  • Load references: expression pattern leaf by section type, all anti-AI pattern leaves, journal template if specified.
  • Detect input characteristics: translationese presence, section type, text length for smart adaptation.
  • Opt-out check: Scan the user's trigger prompt for any of these phrases (case-insensitive, exact phrase match): english only, no bilingual, only english, 不要中文. Store result as bilingual_mode (true/false). This flag governs Step 5 bilingual output below.
  • Record workflow: Append {"skill": "ppw:polish", "ts": "<ISO timestamp>"} to .planning/workflow-memory.json. Create file as [] if missing. Drop oldest entry if log length >= 50.

Step 2 -- Polish:

  • Follow the Core Prompt constraints above as the primary instruction set for this step.
  • Single intelligent pass covering expression, logic, and structure as needed based on text quality.
  • Priority: expression issues first, then logic coherence, then structural adjustments.
  • Apply journal style preferences from loaded template.
  • Avoid anti-AI vocabulary; flag and rewrite AI-sounding phrases.
  • Pay special attention to translationese if detected (literal structures, calques, excessive "of" constructions).
  • For file input: use Edit tool; add % [Polish] Original: <original text> annotation before each modification.
  • For pasted text: present polished output directly in conversation with key changes highlighted.

Step 3 -- Smart Adaptation:

  • Short text (up to ~3 paragraphs): polish entirely in one pass.
  • Long text (4+ paragraphs or multiple \section{} markers): split by section, process sequentially, maintain terminology consistency across sections.

Step 4 -- Summary:

  • Generate concise report: change count, modification types (expression/logic/structure), notes.
  • Include "Recommend running De-AI Skill for further detection check" when substantial rewrites were made.

Step 5 -- Bilingual Display (file input only):

  • If bilingual_mode is true and input was a file: for each paragraph that was modified in Step 2, display a > **[Chinese]**... blockquote in conversation showing the Chinese translation of the polished English text.
  • Use a section header in conversation: "双语对照 / Bilingual Comparison:" before the first blockquote.
  • Format per paragraph: [Chinese] [Chinese translation of the polished paragraph]
  • Do not insert Chinese into the.tex file. The file remains English-only and submission-ready.
  • If bilingual_mode is false (opt-out detected): skip this step entirely.
  • Pasted text input: if bilingual_mode is true, append the > **[Chinese]**... blockquote immediately after each polished paragraph in the conversation output.

Guided Mode

Step 1 -- Collect Context: Same as Quick-fix Step 1, plus confirm scope with user.

Steps 2-4 follow the same pattern: Analyze current text, present numbered checklist of problems found, user selects which to fix ("fix 1, 3" or "all"), apply selected fixes in batch using Edit tool with annotations, report changes. Re-read the file before each step to see the result of previous edits.

StepFocusKey Checks
2. StructureParagraph organization, topic sentences, section flowRedundancy, ordering, paragraph purpose, section transitions
3. LogicArgument coherence, claim support, terminology consistencyArgument chains, evidence links; cross-section coherence when multi-section input
4. ExpressionWord choice, sentence clarity, tone, concisenessAnti-AI patterns, translationese, academic register, hedging calibration

Step 5 -- Summary: Same as Quick-fix Step 4.

Step 6 -- Bilingual Display: Same as Quick-fix Step 5. If bilingual_mode is true, display > **[Chinese]**... blockquotes in conversation for each modified paragraph. If false, skip entirely.

LaTeX Annotation Format

  • Format: % [Polish] Original: <original text> on the line immediately before the replacement text.
  • Multi-line originals: each line gets its own % [Polish] Original: prefix.
  • Annotations are valid LaTeX comments -- the file still compiles with them present.
  • Cleanup: after user confirms acceptance, remove all lines matching ^% \[Polish\] Original: pattern.
  • If existing % [Polish] Original: annotations are found, clean them up before adding new ones.

Output Contract

OutputFormatCondition
polished_textIn-place edits (file) or conversation output (pasted text)Always produced
change_annotationsLaTeX comments (% [Polish] Original:)File input only
summary_reportMarkdown in session (not in file)Always produced
bilingual_conversation> **[Chinese]**... blockquotes in sessionFile input: modified paragraphs only. Pasted text: after each output paragraph. Skipped when opt-out detected.

Edge Cases

SituationHandling
Input too short (single sentence)Polish but warn limited context for structural assessment
No section headers in inputDefault to expression-focused polishing
Pasted text inputOutput directly in conversation; no file operations
File inputUse Edit tool only; never Write (do not overwrite entire file)
Journal specified but no templateRefuse; instruct user to add template at references/journals/[journal].md
Very long input (10+ sections)Process in batches; maintain cross-section terminology awareness
Existing % [Polish] Original: annotationsClean up old annotations before adding new ones
Mixed LaTeX and textPreserve all LaTeX commands; polish surrounding natural language only
Text with obvious translationeseFlag in summary; prioritize translationese fixes in expression pass

Fallbacks

ScenarioFallback
Structured Interaction unavailableAsk 1-3 plain-text questions covering highest-impact gaps
Expression pattern leaf missingUse overview entrypoint for general patterns
Journal template missing for specified journalRefuse -- instruct user to add the template first
Anti-AI patterns file missingProceed without; rely on Claude's vocabulary awareness
File is read-only or Edit failsPresent changes as a diff in conversation; user applies manually

*Skill: polish-skill* *Conventions: references/skill-conventions.md*

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.76%
按下载量换算48

Claude

29.8%
按下载量换算41

Cursor

16.16%
按下载量换算22

Gemini CLI

9.64%
按下载量换算13

安全审计

暂无安全审计结果可展示。

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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